BERTERS: Multimodal Representation Learning for Expert Recommendation System with Transformer
This work addresses the need for accurate expert identification in academic and community question-answering settings, presenting an incremental improvement through multimodal integration.
The paper tackles the problem of expert recommendation by proposing BERTERS, a multimodal classification approach that combines text and graph information, achieving improved performance on multi-label classification and visualization tasks.
The objective of an expert recommendation system is to trace a set of candidates' expertise and preferences, recognize their expertise patterns, and identify experts. In this paper, we introduce a multimodal classification approach for expert recommendation system (BERTERS). In our proposed system, the modalities are derived from text (articles published by candidates) and graph (their co-author connections) information. BERTERS converts text into a vector using the Bidirectional Encoder Representations from Transformer (BERT). Also, a graph Representation technique called ExEm is used to extract the features of candidates from the co-author network. Final representation of a candidate is the concatenation of these vectors and other features. Eventually, a classifier is built on the concatenation of features. This multimodal approach can be used in both the academic community and the community question answering. To verify the effectiveness of BERTERS, we analyze its performance on multi-label classification and visualization tasks.